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Branch and Bound Algorithm for Globally Solving Minimax Linear Fractional Programming
WANG Hui-man, SHEN Pei-ping, LIANG Yu-xin
Chinese Quarterly Journal of Mathematics
2024, 39 (4):
388-398.
DOI: 10.13371/j.cnki.chin.q.j.m.2024.04.005
In this paper, we study the minimax linear fractional programming problem on a non-empty bounded set, called problem (MLFP), and we design a branch and bound algorithm to find a globally optimal solution of (MLFP). Firstly, we convert the problem (MLFP) to a problem (EP2) that is equivalent to it. Secondly, by applying the convex relaxation technique to problem (EP2), a convex quadratic relaxation problem (CQRP) is obtained. Then, the overall framework of the algorithm is given and its convergence is proved, the worst-case iteration number is also estimated. Finally, experimental data are
listed to illustrate the effectiveness of the algorithm.
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